PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inter-domain Gaussian processes for sparse inference using inducing features
Miguel Lazaro-Gredilla and Aníbal R. Figueiras-Vidal
In: Advances in Neural Information Processing Systems (NIPS), 7-10 Dec 2009, Vancouver, Canada.

Abstract

We present a general inference framework for inter-domain Gaussian Processes (GPs) and focus on its usefulness to build sparse GP models. The state-of-the-art sparse GP model introduced by Snelson and Ghahramani in [1] relies on finding a small, representative pseudo data set of m elements (from the same domain as the n available data elements) which is able to explain existing data well, and then uses it to perform inference. This reduces inference and model selection computation time from O(n3) to O(m2n), where m n. Inter-domain GPs can be used to find a (possibly more compact) representative set of features lying in a different domain, at the same computational cost. Being able to specify a different domain for the representative features allows to incorporate prior knowledge about relevant characteristics of data and detaches the functional form of the covariance and basis functions. We will show how previously existing models fit into this framework and will use it to develop two new sparse GP models. Tests on large, representative regression data sets suggest that significant improvement can be achieved, while retaining computational efficiency.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6276
Deposited By:Miguel Lazaro-Gredilla
Deposited On:25 February 2010